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/***********************************************************************
* Software License Agreement (BSD License)
*
* Copyright 2011-2016 Jose Luis Blanco (joseluisblancoc@gmail.com).
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* 1. Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
*
* THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
* OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
* IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
* NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
* THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*************************************************************************/
#include <nanoflann.hpp>
#include "utils.h"
#include <ctime>
#include <cstdlib>
#include <iostream>
using namespace std;
using namespace nanoflann;
void kdtree_save_load_demo(const size_t N)
{
PointCloud<double> cloud;
// Generate points:
generateRandomPointCloud(cloud, N);
double query_pt[3] = { 0.5, 0.5, 0.5 };
// construct a kd-tree index:
typedef KDTreeSingleIndexAdaptor<
L2_Simple_Adaptor<double, PointCloud<double> > ,
PointCloud<double>,
3 /* dim */
> my_kd_tree_t;
// Construct the index and save it:
// --------------------------------------------
{
my_kd_tree_t index(3 /*dim*/, cloud, KDTreeSingleIndexAdaptorParams(10 /* max leaf */) );
index.buildIndex();
FILE *f = fopen("index.bin", "wb");
if (!f) throw std::runtime_error("Error writing index file!");
index.saveIndex(f);
fclose(f);
}
// Load the index from disk:
// --------------------------------------------
{
// Important: construct the index associated to the same dataset, since data points are NOT stored in the binary file.
my_kd_tree_t index(3 /*dim*/, cloud, KDTreeSingleIndexAdaptorParams(10 /* max leaf */) );
FILE *f = fopen("index.bin", "rb");
if (!f) throw std::runtime_error("Error reading index file!");
index.loadIndex(f);
fclose(f);
// do a knn search
const size_t num_results = 1;
size_t ret_index;
double out_dist_sqr;
nanoflann::KNNResultSet<double> resultSet(num_results);
resultSet.init(&ret_index, &out_dist_sqr );
index.findNeighbors(resultSet, &query_pt[0], nanoflann::SearchParams(10));
std::cout << "knnSearch(nn="<<num_results<<"): \n";
std::cout << "ret_index=" << ret_index << " out_dist_sqr=" << out_dist_sqr << endl;
}
}
int main()
{
// Randomize Seed
srand(static_cast<unsigned int>(time(nullptr)));
kdtree_save_load_demo(100000);
return 0;
}